Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities. Consequently, entity clustering serves as an initial step in most analytical studies. This process often employs heuristics grounded in the practices and behaviors of these entities. In this research, we delve into the examination of two widely used heuristics, alongside the introduction of four novel ones. Our contribution includes the introduction of the \textit{clustering ratio}, a metric designed to quantify the reduction in the number of entities achieved by a given heuristic. The assessment of this reduction ratio plays an important role in justifying the selection of a specific heuristic for analytical purposes. Given the dynamic nature of the Bitcoin system, characterized by a continuous increase in the number of entities on the blockchain, and the evolving behaviors of these entities, we extend our study to explore the temporal evolution of the clustering ratio for each heuristic. This temporal analysis enhances our understanding of the effectiveness of these heuristics over time.

翻译：探索比特币区块链中的交易需要检查数亿个实体之间的比特币转移。然而，研究如此庞大的实体数量往往不切实际且消耗资源。因此，实体聚类成为大多数分析研究的初始步骤。该过程通常采用基于这些实体实践和行为的启发式方法。在本研究中，我们深入考察了两种广泛使用的启发式方法，并引入了四种新的启发式方法。我们的贡献包括引入\textit{聚类比率}这一指标，用于量化给定启发式方法所实现的实体数量减少程度。对这一减少比率的评估在论证选择特定启发式方法进行数据分析时起着重要作用。鉴于比特币系统的动态特性，即区块链上实体数量持续增长且这些实体的行为不断演变，我们进一步研究了每种启发式方法聚类比率随时间的变化趋势。这种时间序列分析增强了我们对这些启发式方法长期有效性的理解。